# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Protein data type."""
import dataclasses
import io
from typing import Any, Mapping, Optional

from Bio.PDB import PDBParser
import numpy as np

from . import residue_constants


FeatureDict = Mapping[str, np.ndarray]
ModelOutput = Mapping[str, Any]  # Is a nested dict.

# Complete sequence of chain IDs supported by the PDB format.
PDB_CHAIN_IDS = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789'
PDB_MAX_CHAINS = len(PDB_CHAIN_IDS)  # := 62.


@dataclasses.dataclass(frozen=True)
class Protein:
  """Protein structure representation."""

  # Cartesian coordinates of atoms in angstroms. The atom types correspond to
  # residue_constants.atom_types, i.e. the first three are N, CA, CB.
  atom_positions: np.ndarray  # [num_res, num_atom_type, 3]

  # Amino-acid type for each residue represented as an integer between 0 and
  # 20, where 20 is 'X'.
  aatype: np.ndarray  # [num_res]

  # Binary float mask to indicate presence of a particular atom. 1.0 if an atom
  # is present and 0.0 if not. This should be used for loss masking.
  atom_mask: np.ndarray  # [num_res, num_atom_type]

  # Residue index as used in PDB. It is not necessarily continuous or 0-indexed.
  residue_index: np.ndarray  # [num_res]

  # 0-indexed number corresponding to the chain in the protein that this residue
  # belongs to.
  chain_index: np.ndarray  # [num_res]

  # B-factors, or temperature factors, of each residue (in sq. angstroms units),
  # representing the displacement of the residue from its ground truth mean
  # value.
  b_factors: np.ndarray  # [num_res, num_atom_type]

  def __post_init__(self):
    if len(np.unique(self.chain_index)) > PDB_MAX_CHAINS:
      raise ValueError(
          f'Cannot build an instance with more than {PDB_MAX_CHAINS} chains '
          'because these cannot be written to PDB format.')

  def to_dict(self):
    return dataclasses.asdict(self)


def make_pseudo_protein_dict(L):
  return {
      'atom_positions': np.zeros((L, 37, 3), dtype=np.float32),
      'aatype': np.zeros((L,), dtype=np.int32),
      'atom_mask': np.zeros((L, 37), dtype=np.float32),
      'residue_index': np.zeros((L,), dtype=np.int32),
      'chain_index': np.zeros((L,), dtype=np.int32),
      'b_factors': np.zeros((L, 37), dtype=np.float32),
  }


def from_pdb_file(pdb_file: str, chain_id: Optional[str] = None) -> Protein:
  """Takes a PDB file and constructs a Protein object.

  WARNING: All non-standard residue types will be converted into UNK. All
    non-standard atoms will be ignored.

  Args:
    pdb_file: The path to the pdb file
    chain_id: If chain_id is specified (e.g. A), then only that chain
      is parsed. Otherwise all chains are parsed.

  Returns:
    A new `Protein` parsed from the pdb file.
  """
  with open(pdb_file, 'r') as f:
    return from_pdb_string(f.read(), chain_id)

def from_pdb_string(pdb_str: str, chain_id: Optional[str] = None) -> Protein:
  """Takes a PDB string and constructs a Protein object.

  WARNING: All non-standard residue types will be converted into UNK. All
    non-standard atoms will be ignored.

  Args:
    pdb_str: The contents of the pdb file
    chain_id: If chain_id is specified (e.g. A), then only that chain
      is parsed. Otherwise all chains are parsed.

  Returns:
    A new `Protein` parsed from the pdb contents.
  """
  pdb_fh = io.StringIO(pdb_str)
  parser = PDBParser(QUIET=True)
  structure = parser.get_structure('none', pdb_fh)
  models = list(structure.get_models())
  if len(models) != 1:
    print(f'Warning: Only single model PDBs are supported. Found {len(models)} models.')
  model = models[0]

  atom_positions = []
  aatype = []
  atom_mask = []
  residue_index = []
  chain_ids = []
  b_factors = []

  for chain in model:
    if chain_id is not None and chain.id != chain_id:
      continue
    for res in chain:
      if res.id[2] != ' ':
        raise ValueError(
            f'PDB contains an insertion code at chain {chain.id} and residue '
            f'index {res.id[1]}. These are not supported.')
      res_shortname = residue_constants.restype_3to1.get(res.resname, 'X')
      restype_idx = residue_constants.restype_order.get(
          res_shortname, residue_constants.restype_num)
      pos = np.zeros((residue_constants.atom_type_num, 3))
      mask = np.zeros((residue_constants.atom_type_num,))
      res_b_factors = np.zeros((residue_constants.atom_type_num,))
      for atom in res:
        if atom.name not in residue_constants.atom_types:
          continue
        pos[residue_constants.atom_order[atom.name]] = atom.coord
        mask[residue_constants.atom_order[atom.name]] = 1.
        res_b_factors[residue_constants.atom_order[atom.name]] = atom.bfactor
      if np.sum(mask) < 0.5:
        # If no known atom positions are reported for the residue then skip it.
        continue
      aatype.append(restype_idx)
      atom_positions.append(pos)
      atom_mask.append(mask)
      residue_index.append(res.id[1])
      chain_ids.append(chain.id)
      b_factors.append(res_b_factors)

  # Chain IDs are usually characters so map these to ints.
  unique_chain_ids = np.unique(chain_ids)
  chain_id_mapping = {cid: n for n, cid in enumerate(unique_chain_ids)}
  chain_index = np.array([chain_id_mapping[cid] for cid in chain_ids])

  return Protein(
      atom_positions=np.array(atom_positions),
      atom_mask=np.array(atom_mask),
      aatype=np.array(aatype),
      residue_index=np.array(residue_index),
      chain_index=chain_index,
      b_factors=np.array(b_factors))


def _chain_end(atom_index, end_resname, chain_name, residue_index) -> str:
  chain_end = 'TER'
  return (f'{chain_end:<6}{atom_index:>5}      {end_resname:>3} '
          f'{chain_name:>1}{residue_index:>4}')


def to_pdb(prot: Protein, model=1, add_end=True) -> str:
  """Converts a `Protein` instance to a PDB string.

  Args:
    prot: The protein to convert to PDB.

  Returns:
    PDB string.
  """
  restypes = residue_constants.restypes + ['X']
  res_1to3 = lambda r: residue_constants.restype_1to3.get(restypes[r], 'UNK')
  atom_types = residue_constants.atom_types

  pdb_lines = []

  atom_mask = prot.atom_mask
  aatype = prot.aatype
  atom_positions = prot.atom_positions
  residue_index = prot.residue_index.astype(int)
  chain_index = prot.chain_index.astype(int)
  b_factors = prot.b_factors

  if np.any(aatype > residue_constants.restype_num):
    raise ValueError('Invalid aatypes.')

  # Construct a mapping from chain integer indices to chain ID strings.
  chain_ids = {}
  for i in np.unique(chain_index):  # np.unique gives sorted output.
    if i >= PDB_MAX_CHAINS:
      raise ValueError(
          f'The PDB format supports at most {PDB_MAX_CHAINS} chains.')
    chain_ids[i] = PDB_CHAIN_IDS[i]

  pdb_lines.append(f'MODEL     {model}')
  atom_index = 1
  last_chain_index = chain_index[0]
  # Add all atom sites.
  for i in range(aatype.shape[0]):
    # Close the previous chain if in a multichain PDB.
    if last_chain_index != chain_index[i]:
      pdb_lines.append(_chain_end(
          atom_index, res_1to3(aatype[i - 1]), chain_ids[chain_index[i - 1]],
          residue_index[i - 1]))
      last_chain_index = chain_index[i]
      atom_index += 1  # Atom index increases at the TER symbol.

    res_name_3 = res_1to3(aatype[i])
    for atom_name, pos, mask, b_factor in zip(
        atom_types, atom_positions[i], atom_mask[i], b_factors[i]):
      if mask < 0.5:
        continue
      
      # skip CB for GLY
      if res_name_3 == 'GLY' and atom_name == 'CB':
        continue

      record_type = 'ATOM'
      name = atom_name if len(atom_name) == 4 else f' {atom_name}'
      alt_loc = ''
      insertion_code = ''
      occupancy = 1.00
      element = atom_name[0]  # Protein supports only C, N, O, S, this works.
      charge = ''
      # PDB is a columnar format, every space matters here!
      atom_line = (f'{record_type:<6}{atom_index:>5} {name:<4}{alt_loc:>1}'
                   f'{res_name_3:>3} {chain_ids[chain_index[i]]:>1}'
                   f'{residue_index[i]:>4}{insertion_code:>1}   '
                   f'{pos[0]:>8.3f}{pos[1]:>8.3f}{pos[2]:>8.3f}'
                   f'{occupancy:>6.2f}{b_factor:>6.2f}          '
                   f'{element:>2}{charge:>2}')
      pdb_lines.append(atom_line)
      atom_index += 1

  # Close the final chain.
  pdb_lines.append(_chain_end(atom_index, res_1to3(aatype[-1]),
                              chain_ids[chain_index[-1]], residue_index[-1]))
  pdb_lines.append('ENDMDL')
  if add_end:
    pdb_lines.append('END')

  # Pad all lines to 80 characters.
  pdb_lines = [line.ljust(80) for line in pdb_lines]
  return '\n'.join(pdb_lines) + '\n'  # Add terminating newline.


def ideal_atom_mask(prot: Protein) -> np.ndarray:
  """Computes an ideal atom mask.

  `Protein.atom_mask` typically is defined according to the atoms that are
  reported in the PDB. This function computes a mask according to heavy atoms
  that should be present in the given sequence of amino acids.

  Args:
    prot: `Protein` whose fields are `numpy.ndarray` objects.

  Returns:
    An ideal atom mask.
  """
  return residue_constants.STANDARD_ATOM_MASK[prot.aatype]


def from_prediction(
    features: FeatureDict,
    result: ModelOutput,
    b_factors: Optional[np.ndarray] = None,
    remove_leading_feature_dimension: bool = True) -> Protein:
  """Assembles a protein from a prediction.

  Args:
    features: Dictionary holding model inputs.
    result: Dictionary holding model outputs.
    b_factors: (Optional) B-factors to use for the protein.
    remove_leading_feature_dimension: Whether to remove the leading dimension
      of the `features` values.

  Returns:
    A protein instance.
  """
  fold_output = result['structure_module']

  def _maybe_remove_leading_dim(arr: np.ndarray) -> np.ndarray:
    return arr[0] if remove_leading_feature_dimension else arr

  if 'asym_id' in features:
    chain_index = _maybe_remove_leading_dim(features['asym_id'])
  else:
    chain_index = np.zeros_like(_maybe_remove_leading_dim(features['aatype']))

  if b_factors is None:
    b_factors = np.zeros_like(fold_output['final_atom_mask'])

  return Protein(
      aatype=_maybe_remove_leading_dim(features['aatype']),
      atom_positions=fold_output['final_atom_positions'],
      atom_mask=fold_output['final_atom_mask'],
      residue_index=_maybe_remove_leading_dim(features['residue_index']) + 1,
      chain_index=chain_index,
      b_factors=b_factors)